The present invention relates to defect inspections such as pattern inspection for detecting defective states, for example, short (short-circuiting) or open (wire disconnections), of the pattern to be inspected and particle inspection for detecting foreign particles on the pattern. More particularly, the invention relates to: the assistance technology for assigning (setting) the inspection recipes used for defect inspection tools which perform defect inspections on the patterns of semiconductor wafers, liquid-crystal displays, photomasks, and the like; and the technology for classifying detected defects.
As the tendencies towards shorter semiconductor product-life cycles and towards the shift to flexible manufacturing primarily of system LSIs are increasing, the demand for early establishment of mass-production process conditions is increasingly growing. Inspection tools for visually inspecting semiconductor products, acquiring information on the occurrence status of defects, and obtaining guidelines on adjustment of process conditions or detecting unusual process states, are extremely important in semiconductor manufacturing processes. Darkfield and brightfield inspection tools or electron-beam-type pattern inspection tools/defect reviewing tools are already commercialized as semiconductor visual inspection tools.
The parameter assignment procedure using such a conventional inspection tool is described below. Parameters are broadly divided into image acquisition conditions parameters (hereinafter, referred to as image acquisition parameters), and image-processing conditions (inspection conditions) assignment parameters (inspection parameters). Image acquisition parameters are parameters for detecting an appropriate image. For example, for an optical visual inspection tool, optical conditions, such as a magnification (pixel size), a focal position offset quantity, illumination wavelength and light intensity, various filtering conditions, and an aperture stop value, are equivalent to the image acquisition parameters mentioned above.
Inspection parameters are parameters that need to be assigned for appropriate processing of a detected image. For example, binary thresholds for extracting only defective portions from the defected image in a binary format, denoising-filter size thresholds for removing as noise a minute-area region independent of defects, a parameter for reducing sensitivity at an edge of the wafer, and other data are equivalent to the above inspection parameters.
In conventional inspection tools of this type, such an inspection parameter assignment procedure as shown in FIG. 31 has been used. This procedure is described below. First, in step S3101, the wafer to be inspected is set up for inspection. Image acquisition parameters are tentatively assigned in step S3102, and in step S3103, a suitable location on the pattern to be inspected is imaged and image quality is confirmed. For example, if the image is too dark, the light intensity is increased, or if the image is too low in contrast level, optical conditions such as filtering or aperture stop conditions are modified or focus is adjusted.
The parameter assignment and image quality confirmation steps mentioned above are repeated until a desired image has been obtained, and when the desired image is obtained, the above-assigned image acquisition parameters are registered in step S3104. Following this, tentatively assigned inspection parameters are used in step S3105 to perform provisional inspections (test inspection) in step S3106.
After the inspections have been performed, the positions of the defects detected are reviewed in step S3107 and the assignments of the inspection parameters are verified in step S3108. At this time, the following corrective actions are conceivable to be taken: for example, if false defects, not real defects, are detected in greater numbers than assumed, when sensitivity is too high, binary thresholds are increased. If the number of very small defects is greater than necessary, the denoising-filter size is increased.
By repeating the above-described inspection parameter reassignment, test inspection re-execution, and inspection result reconfirmation steps until desired detection sensitivity has been obtained, inspection parameters are determined and the parameters are registered in order for the assignment thereof to be completed in step S3109.
Japanese Patent Laid-open No. 2001-337047 discloses an optimum recipe-creating method in which a simulated defect wafer for obtaining desired detection sensitivity is created, the method not depending on the technical skill level of the operator.
In addition, as regards the classification of defects, Japanese Patent No. 3139998 discloses a method in which the section to be analyzed for defect classification is sampled from detected defects and defect detection and part of defect classification are concurrently performed.
In both types of conventional art mentioned above, since the procedure for performing inspections each time inspection conditions are assigned and then reviewing the results must be repeated until desired image quality and inspection sensitivity have been obtained, and since all steps, except for test inspection, must be manually performed by an operator, there has been the problem that great deals of labor and time are required until a recipe has been determined. In the reviewing operations, there has been incurred the waste that the same real defect (or false defect) is reviewed with each modification of conditions. Also, assignment operations have the problem that since it is not easy to compare results between sets of inspection conditions, it is difficult to select the optimum conditions. Assignment of contrast threshold data, in particular, has relied on the experience of the operator and required a trial-and-error approach.
In addition, the method of sampling the defects to be classified during defect classification has presented the problem that: since the classification becomes a time-consuming step because of too low throughput of the classification/analysis processor, although this conventional method is effective in the case where the analyzing time significantly exceeds the inspection time even by parallel execution of the inspection and the classification, not all defects can be classified and this causes the omission of classification of a target defect to which the user is originally to pay attention.
The above conventional method has also posed the problem that since the defect distribution state of the entire wafer cannot be accurately identified, this distribution state is only estimated.